Metabolomic analysis of plasma from Alzheimer disease patients

Abstract

Alzheimer Disease is a degenerative disease characterized by progressive impairment of cognitive function. The main feature of AD the generation of an abnormal peptide, beta amyloid 42 (Ab42) from Amyloid Precursor Protein (APP). Ab42 is the main constituent of neurotangles and amyloid plaques, microscopic lesions found in AD patients brain. Ab42 triggers an inflammatory response that is responsible for most of the observed tissue damage. The diagnosis of AD is a complex task, mostly based on imaging techniques and clinical evaluation of the patient’s neurological and cognitive functions. The search for plasma biomarkers able to detect early mild cognitive impairment is one of the recent attempt the supply the clinician with new diagnostic tools. In this study we focused on a gas-chromatography mass-spectrometry (GC-MS) analysis coupled to chemometric automated metabolomic analysis of AD plasma samples compared with plasma of healthy subjects of comparable age and gender. Sera from twenty AD and twenty controls have been subjects to a procedure optimized to extract short chain organic acids, sugars and some fatty acids that can be detected by GC coupled to ion trap/MS detection. The method allowed the detection of over five thousands of individual ions that have been collected and measured by the XCMS software. After automated peak detection and alignment by XCMS, peaks have been normalized by a set of internal standards (C13 Leucine, C13 palmitic acid) and clustered into putative compounds by a homemade software. About 80 compounds were differentially expressed between AD and controls. After manual verification of the automated data, most of the compounds have been excluded since they represent column leakage or method artifacts, but some compounds represent true plasma constituents that are under investigation. Current findings will be presented after putative compound identification by the AMDIS/NIST software.